Script to analyze larval size, symbiont density, and examine correlations between physiological responses.
Set up workspace, set options, and load required packages.
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
# Larval size data
size <- read_csv("Mcap2020/Data/Physiology/Size/larval_size.csv")
#metadata
metadata <- read_csv("Mcap2020/Data/lifestage_metadata.csv")
size <- left_join(size, metadata)
size$hpf <- as.factor(size$hpf)
Prep data frame.
# Calculate mean counts for each sample
size <- size %>%
dplyr::select(tube.ID, lifestage, replicate, `area (mm)`, hpf, group)%>%
drop_na()%>% #remove na's that could not be measured
rename(area=`area (mm)`) #rename column
size$lifestage<-as.factor(size$lifestage)
Plot data with mean and standard error for each lifestage.
size %>%
ggplot(aes(x = lifestage, y = area, color = lifestage)) +
labs(x = "",y = "Mean Larval Size (mm^2)") +
geom_jitter(width = 0.1) + # Plot all points
stat_summary(fun.data = mean_cl_normal, fun.args = list(mult = 1), # Plot standard error
geom = "errorbar", color = "black", width = 0.5) +
stat_summary(fun = mean, geom = "point", color = "black") + # Plot mean
theme_classic()
Present means and standard error of each group and save summary table
size%>%
group_by(lifestage, hpf)%>%
summarise(n=length(area),
Mean=format(round(mean(area), 3), 3),
SE=format(round(sd(area)/sqrt(length(area)),3),3))%>%
rename(Lifestage=lifestage, HPF=hpf)%>%
kbl(caption="Descriptive statistics of larval size across ontogeny")%>%
kable_classic(full_width=FALSE, html_font="Arial")%>%
row_spec(0, bold = TRUE)
| Lifestage | HPF | n | Mean | SE |
|---|---|---|---|---|
| Egg Fertilized | 1 | 40 | 0.19 | 0.004 |
| Embryo 1 | 5 | 40 | 0.184 | 0.003 |
| Larvae 1 | 38 | 40 | 0.258 | 0.005 |
| Larvae 2 | 65 | 40 | 0.158 | 0.003 |
| Larvae 3 | 93 | 40 | 0.184 | 0.003 |
| Larvae 4 | 163 | 40 | 0.193 | 0.004 |
| Larvae 5 | 183 | 40 | 0.196 | 0.005 |
| Larvae 6 | 231 | 40 | 0.312 | 0.014 |
| Recruit 1 | 183 | 40 | 0.193 | 0.006 |
| Recruit 2 | 231 | 33 | 0.27 | 0.021 |
#need to output to csv
size%>%
group_by(lifestage, hpf)%>%
summarise(n=length(area),
Mean=format(round(mean(area), 3), 3),
SE=format(round(sd(area)/sqrt(length(area)),3),3))%>%
rename(Lifestage=lifestage, HPF=hpf)%>%
write_csv(., "Mcap2020/Output/Physiology/larval_size_table.csv")
Plot data as a scatterplot
size$hpf<-as.factor(size$hpf)
size_plot<-size %>%
ggplot(., aes(x = hpf, y = area)) +
#geom_boxplot(outlier.size = 0) +
geom_smooth(method="loess", se=TRUE, fullrange=TRUE, level=0.95, color="black") +
geom_point(aes(fill=group, group=group), pch = 21, size=4, position = position_jitterdodge(0.1)) +
xlab("Hours Post-Fertilization") +
scale_fill_manual(name="Lifestage", values=c("#8C510A", "#DFC27D","#80CDC1", "#003C30"))+
ylab(expression(bold(paste("Planar Size (mm"^2, ")"))))+
ylim(0,1)+
theme_classic() +
theme(
legend.position="right",
axis.title=element_text(face="bold", size=14),
axis.text=element_text(size=12, color="black"),
legend.title=element_text(face="bold", size=14),
legend.text=element_text(size=12)
); size_plot
#EGG: #8C510A
#EMBRYO: #DFC27D
#LARVAE: #80CDC1
#RECRUIT: #003C30
size_plot2<-size %>%
ggplot(., aes(x = hpf, y = area)) +
geom_boxplot(aes(color=group), outlier.size = 0, lwd=1) +
geom_point(aes(fill=group), pch = 21, size=2, position = position_jitterdodge(0.1)) +
xlab("Hours Post-Fertilization") +
scale_fill_manual(name="Lifestage", values=c("#8C510A", "#DFC27D","#80CDC1", "#003C30"))+
scale_color_manual(name="Lifestage", values=c("#8C510A", "#DFC27D","#80CDC1", "#003C30"))+
ylab(expression(bold(paste("Planar Size (mm"^2, ")"))))+
ylim(0, 0.8)+
theme_classic() +
#geom_text(label="A", x=1, y=2500, size=4, color="black")+ #egg
#geom_text(label="A", x=2, y=2500, size=4, color="black")+ #embryo 1
#geom_text(label="A", x=3, y=2500, size=4, color="black")+ #larvae 1
#geom_text(label="AB", x=4, y=4100, size=4, color="black")+ #larvae 2
#geom_text(label="AB", x=5, y=4100, size=4, color="black")+ #larvae 3
#geom_text(label="AB", x=6, y=4100, size=4, color="black")+ #larvae 4
#geom_text(label="BC", x=6.8, y=4500, size=4, color="black")+ #larvae 5
#geom_text(label="CD", x=7.2, y=6500, size=4, color="black")+ #recruit 1
#geom_text(label="D", x=7.8, y=6500, size=4, color="black")+ #larvae6
#geom_text(label="D", x=8.2, y=8700, size=4, color="black")+ #recruit2
theme(
legend.position="right",
axis.title=element_text(face="bold", size=14),
axis.text=element_text(size=12, color="black"),
legend.title=element_text(face="bold", size=14)
); size_plot2
Run lmer on cells per larvae by sampling point, specified by sequence of samples taken (life stage, hpf). Use tube ID as random effect.
size_model<-lmer(area~lifestage + (1|tube.ID), data=size)
summary(size_model)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: area ~ lifestage + (1 | tube.ID)
## Data: size
##
## REML criterion at convergence: -1149.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9918 -0.4117 -0.0292 0.3067 6.2690
##
## Random effects:
## Groups Name Variance Std.Dev.
## tube.ID (Intercept) 0.000000 0.00000
## Residual 0.002646 0.05144
## Number of obs: 393, groups: tube.ID, 40
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.190150 0.008133 383.000000 23.381 < 2e-16 ***
## lifestageEmbryo 1 -0.005650 0.011501 383.000000 -0.491 0.62354
## lifestageLarvae 1 0.068075 0.011501 383.000000 5.919 7.21e-09 ***
## lifestageLarvae 2 -0.031825 0.011501 383.000000 -2.767 0.00593 **
## lifestageLarvae 3 -0.006350 0.011501 383.000000 -0.552 0.58120
## lifestageLarvae 4 0.003025 0.011501 383.000000 0.263 0.79268
## lifestageLarvae 5 0.006000 0.011501 383.000000 0.522 0.60220
## lifestageLarvae 6 0.122175 0.011501 383.000000 10.623 < 2e-16 ***
## lifestageRecruit 1 0.003225 0.011501 383.000000 0.280 0.77932
## lifestageRecruit 2 0.079395 0.012096 383.000000 6.564 1.71e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) lfstE1 lfstL1 lfstL2 lfstL3 lfstL4 lfstL5 lfstL6 lfstR1
## lfstgEmbry1 -0.707
## lifestgLrv1 -0.707 0.500
## lifestgLrv2 -0.707 0.500 0.500
## lifestgLrv3 -0.707 0.500 0.500 0.500
## lifestgLrv4 -0.707 0.500 0.500 0.500 0.500
## lifestgLrv5 -0.707 0.500 0.500 0.500 0.500 0.500
## lifestgLrv6 -0.707 0.500 0.500 0.500 0.500 0.500 0.500
## lifstgRcrt1 -0.707 0.500 0.500 0.500 0.500 0.500 0.500 0.500
## lifstgRcrt2 -0.672 0.475 0.475 0.475 0.475 0.475 0.475 0.475 0.475
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular
qqPlot(residuals(size_model))
## [1] 361 383
leveneTest(residuals(size_model)~lifestage, data=size)
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 9 12.196 < 2.2e-16 ***
## 383
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(size_model, type=2)
## Type II Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## lifestage 0.83148 0.092387 9 383 34.92 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Violation in normality and variance assumptions. Conduct non-parametric test (Kruskal Wallis).
kruskal.test(area~lifestage, data=size)
##
## Kruskal-Wallis rank sum test
##
## data: area by lifestage
## Kruskal-Wallis chi-squared = 191.71, df = 9, p-value < 2.2e-16
Significant difference in size between lifestages.
View posthoc comparisons for differences between lifestages.
emm = emmeans(size_model, ~ lifestage)
cld(emm, Letters=c(LETTERS)) #letter display
## lifestage emmean SE df lower.CL upper.CL .group
## Larvae 2 0.158 0.00813 29.0 0.142 0.175 A
## Larvae 3 0.184 0.00813 29.0 0.167 0.200 A
## Embryo 1 0.184 0.00813 29.0 0.168 0.201 A
## Egg Fertilized 0.190 0.00813 29.0 0.174 0.207 A
## Larvae 4 0.193 0.00813 29.0 0.177 0.210 A
## Recruit 1 0.193 0.00813 29.0 0.177 0.210 A
## Larvae 5 0.196 0.00813 29.0 0.180 0.213 A
## Larvae 1 0.258 0.00813 29.0 0.242 0.275 B
## Recruit 2 0.270 0.00897 38.9 0.251 0.288 B
## Larvae 6 0.312 0.00813 29.0 0.296 0.329 C
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 10 estimates
## significance level used: alpha = 0.05
## NOTE: Compact letter displays can be misleading
## because they show NON-findings rather than findings.
## Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
# Cell count data
sym_counts <- read_csv("Mcap2020/Data/Physiology/CellDensity/symbiont.counts.csv")
sym_counts <- left_join(sym_counts, metadata)
sym_counts$hpf <- as.factor(sym_counts$hpf)
Calculate cells and normalize to either planar size (eggs through metamorphosed recruits) or surface area (attached recruits)
# Calculate mean counts for each sample
df <- sym_counts %>%
dplyr::select(tube.ID, num.squares, matches("count[1-6]")) %>%
gather("rep", "count", -tube.ID, -num.squares) %>%
group_by(tube.ID, num.squares) %>%
summarise(mean_count = mean(count, na.rm = TRUE))
#match in identifying information
df$lifestage<-sym_counts$lifestage[match(df$tube.ID, sym_counts$tube.ID)]
df$total.volume.ul<-sym_counts$total.volume.ul[match(df$tube.ID, sym_counts$tube.ID)]
df$num.individuals<-sym_counts$num.individuals[match(df$tube.ID, sym_counts$tube.ID)]
df$surface.area<-sym_counts$surface.area[match(df$tube.ID, sym_counts$tube.ID)]
df$hpf<-sym_counts$hpf[match(df$tube.ID, sym_counts$tube.ID)]
df$group<-sym_counts$group[match(df$tube.ID, sym_counts$tube.ID)]
df$lifestage<-as.factor(df$lifestage)
df$group<-as.factor(df$group)
# Normalize counts by homogenat volume and surface area
df <- df %>%
mutate(cells.mL = mean_count * 10000 / num.squares,
cells = cells.mL * (total.volume.ul/1000),
cells.ind = cells / num.individuals,
cells.mm = cells / surface.area)
sym_counts<-df
Plot data with mean and standard error for larvae through metamorphosis (these counts have cells/individual). Plot attached recruits separately, these values are in cells per mm2. We will plot cells per unit surface area for all stages in later analyses.
Display cells per individual.
sym_counts %>%
filter(!group=="Attached Recruit")%>%
ggplot(aes(x = lifestage, y = cells.ind, color = lifestage)) +
labs(x = "",y = "Cell Density per larva") +
geom_jitter(width = 0.1) + # Plot all points
stat_summary(fun.data = mean_cl_normal, fun.args = list(mult = 1), # Plot standard error
geom = "errorbar", color = "black", width = 0.5) +
stat_summary(fun = mean, geom = "point", color = "black") + # Plot mean
theme_classic()
Display cell density per mm2 in attached recruit plugs. Plug 1 = 48 hps, Plug 2 = 72 hps, Plug 3 = 96 hps
sym_counts %>%
filter(group=="Attached Recruit")%>%
ggplot(aes(x = lifestage, y = cells.mm, color = lifestage)) +
labs(x = "",y = "Cell Density per mm2") +
geom_jitter(width = 0.1) + # Plot all points
#stat_summary(fun.data = mean_cl_normal, fun.args = list(mult = 1), # Plot standard error
#geom = "errorbar", color = "black", width = 0.5) +
#stat_summary(fun.y = mean, geom = "point", color = "black") + # Plot mean
theme_classic()
Present means and standard error of each group and save summary table.
sym_counts%>%
group_by(group, hpf, lifestage)%>%
summarise(n=length(cells.ind),
Mean=format(round(mean(cells.ind), 0), 0),
SE=format(round(sd(cells.ind)/sqrt(length(cells.ind)),0),0))%>%
rename(Lifestage=group, HPF=hpf)%>%
kbl(caption="Descriptive statistics of Symbiodiniaceae cell densities per larva across ontogeny")%>%
kable_classic(full_width=FALSE, html_font="Arial")%>%
row_spec(0, bold = TRUE)
| Lifestage | HPF | lifestage | n | Mean | SE |
|---|---|---|---|---|---|
| Attached Recruit | 183 | Plug 1 | 3 | NA | NA |
| Attached Recruit | 231 | Plug 2 | 2 | NA | NA |
| Attached Recruit | 255 | Plug 3 | 3 | NA | NA |
| Egg | 1 | Egg Fertilized | 4 | 1472 | 125 |
| Embryo | 5 | Embryo 1 | 4 | 1831 | 124 |
| Embryo | 38 | Larvae 1 | 4 | 1371 | 242 |
| Embryo | 65 | Larvae 2 | 4 | 2646 | 238 |
| Larvae | 93 | Larvae 3 | 4 | 2692 | 347 |
| Larvae | 163 | Larvae 4 | 4 | 2848 | 206 |
| Larvae | 183 | Larvae 5 | 4 | 3474 | 134 |
| Larvae | 231 | Larvae 6 | 4 | 5142 | 386 |
| Metamorphosed Recruit | 183 | Recruit 1 | 4 | 4829 | 480 |
| Metamorphosed Recruit | 231 | Recruit 2 | 4 | 6424 | 659 |
sym_counts%>%
group_by(group, hpf, lifestage)%>%
summarise(n=length(cells.mm),
Mean=format(round(mean(cells.mm), 0), 0),
SE=format(round(sd(cells.mm)/sqrt(length(cells.mm)),0),0))%>%
rename(Lifestage=group, HPF=hpf)%>%
kbl(caption="Descriptive statistics of Symbiodiniaceae cell densities per mm2 across ontogeny")%>%
kable_classic(full_width=FALSE, html_font="Arial")%>%
row_spec(0, bold = TRUE)
| Lifestage | HPF | lifestage | n | Mean | SE |
|---|---|---|---|---|---|
| Attached Recruit | 183 | Plug 1 | 3 | 21503 | 3084 |
| Attached Recruit | 231 | Plug 2 | 2 | 30977 | 185 |
| Attached Recruit | 255 | Plug 3 | 3 | 42014 | 3847 |
| Egg | 1 | Egg Fertilized | 4 | NA | NA |
| Embryo | 5 | Embryo 1 | 4 | NA | NA |
| Embryo | 38 | Larvae 1 | 4 | NA | NA |
| Embryo | 65 | Larvae 2 | 4 | NA | NA |
| Larvae | 93 | Larvae 3 | 4 | NA | NA |
| Larvae | 163 | Larvae 4 | 4 | NA | NA |
| Larvae | 183 | Larvae 5 | 4 | NA | NA |
| Larvae | 231 | Larvae 6 | 4 | NA | NA |
| Metamorphosed Recruit | 183 | Recruit 1 | 4 | NA | NA |
| Metamorphosed Recruit | 231 | Recruit 2 | 4 | NA | NA |
#need to output to csv
sym_counts%>%
group_by(group, hpf, lifestage)%>%
summarise(n=length(cells.ind),
Mean=format(round(mean(cells.ind), 0), 0),
SE=format(round(sd(cells.ind)/sqrt(length(cells.ind)),0),0))%>%
rename(group=group, HPF=hpf)%>%
write_csv(., "Mcap2020/Output/Physiology/cell_density_table.csv")
Plot data as a scatterplot
sym_counts$hpf<-as.numeric(as.character(sym_counts$hpf))
symb_plot<-sym_counts %>%
filter(!group=="Attached Recruit")%>%
droplevels()%>%
ggplot(., aes(x = hpf, y = cells.ind)) +
#geom_boxplot(outlier.size = 0) +
geom_smooth(method="lm", se=TRUE, fullrange=TRUE, level=0.95, color="black") +
geom_point(aes(fill=group, group=group), pch = 21, size=4, position = position_jitterdodge(5)) +
xlab("Hours Post-Fertilization") +
scale_fill_manual(name="Lifestage", values=c("#8C510A", "#DFC27D","#80CDC1", "#003C30"))+
ylab(expression(bold(paste("Symbiont cells individual"^-1))))+
ylim(0,9000)+
theme_classic() +
theme(
legend.position="right",
axis.title=element_text(face="bold", size=14),
axis.text=element_text(size=12, color="black"),
legend.title=element_text(face="bold", size=14),
legend.text=element_text(size=12)
); symb_plot
#EGG: #8C510A
#EMBRYO: #DFC27D
#LARVAE: #80CDC1
#RECRUIT: #003C30
#ATTACHED: #BA55D3
Plot data as box plot
symb_plot2<-sym_counts %>%
filter(!group=="Attached Recruit")%>%
droplevels()%>%
ggplot(., aes(x = as.factor(hpf), y = cells.ind)) +
geom_boxplot(aes(color=group), outlier.size = 0, lwd=1) +
#geom_smooth(method="loess", se=TRUE, fullrange=TRUE, level=0.95, color="black") +
geom_point(aes(fill=group), pch = 21, size=4, position = position_jitterdodge(0.2)) +
xlab("Hours Post-Fertilization") +
scale_fill_manual(name="Lifestage", values=c("#8C510A", "#DFC27D","#80CDC1", "#003C30"))+
scale_color_manual(name="Lifestage", values=c("#8C510A", "#DFC27D","#80CDC1", "#003C30"), guide="none")+
ylab(expression(bold(paste("Symbiont cells individual"^-1))))+
ylim(0,9000)+
theme_classic() +
theme(
legend.position="right",
axis.title=element_text(face="bold", size=14),
axis.text=element_text(size=12, color="black"),
legend.title=element_text(face="bold", size=14)
); symb_plot2
Run ANOVA on cells per larvae by sampling point, specified by sequence of samples taken (life stage, hpf).
sym_ind_model_data<-sym_counts%>%
filter(!group=="Attached Recruit")%>%
droplevels()
sym_ind_model<-aov(cells.ind~lifestage, data=sym_ind_model_data)
summary(sym_ind_model)
## Df Sum Sq Mean Sq F value Pr(>F)
## lifestage 9 102932981 11436998 25.06 1.34e-11 ***
## Residuals 30 13689458 456315
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
qqPlot(residuals(sym_ind_model))
## [1] 33 30
leveneTest(residuals(sym_ind_model)~lifestage, data=sym_ind_model_data)
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 9 1.7683 0.1166
## 30
Both normality and homogeneity of variance pass.
There is a significant effect of lifestage on cell densities. View posthoc comparisons for differences between lifestages.
emm = emmeans(sym_ind_model, ~ lifestage)
cld(emm, Letters=c(LETTERS)) #letter display
## lifestage emmean SE df lower.CL upper.CL .group
## Larvae 1 1371 338 30 681 2061 A
## Egg Fertilized 1472 338 30 782 2161 A
## Embryo 1 1831 338 30 1141 2521 A
## Larvae 2 2646 338 30 1956 3335 AB
## Larvae 3 2692 338 30 2002 3382 AB
## Larvae 4 2848 338 30 2158 3538 AB
## Larvae 5 3474 338 30 2784 4163 BC
## Recruit 1 4829 338 30 4139 5519 CD
## Larvae 6 5142 338 30 4452 5831 D
## Recruit 2 6424 338 30 5734 7114 D
##
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 10 estimates
## significance level used: alpha = 0.05
## NOTE: Compact letter displays can be misleading
## because they show NON-findings rather than findings.
## Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
Output data to file.
sym_counts %>%
write_csv(., file = "Mcap2020/Output/Physiology/calculated_densities.csv")
First, test for correlation between symbiont cell density and larval size to see if there is a relationship.
Generate data frame with summarised size and cell density information for each time point from eggs to metamorphosed recruits, because we have data for size and counts for each sample. We do not include attached recruits here yet, becuase we cannot calculate densities per individual.
#read in data frame generated in previous chunk
sym_counts<-sym_counts%>%
dplyr::select(tube.ID, lifestage, group, hpf, cells.ind, cells.mm)
#grab size data
area<-size%>%
group_by(tube.ID)%>%
summarise(mean_area=mean(area, na.rm=TRUE))
area$tube.ID<-as.factor(area$tube.ID)
sym_counts$hpf<-as.factor(sym_counts$hpf)
corr<-left_join(sym_counts, area)
Generate number of symbiont cells per mm^2 area for each tube.
corr<-corr%>%
mutate(counts_area=cells.ind/mean_area)%>%
mutate(counts_area=ifelse(is.na(counts_area), cells.mm, counts_area)) #add attached recruit data already calculated as cells per mm2
Plot correlation between cell counts (cells per individual) and size (area mm^2).
correlation<-corr %>%
filter(!group=="Attached Recruit")%>%
ggplot(., aes(x = mean_area, y = cells.ind)) +
#geom_smooth(method="lm", se=TRUE, fullrange=TRUE, level=0.95, color="black", fill="gray") +
geom_point(aes(fill=group), pch = 21, size=4) +
xlab(expression(bold(paste("Larval Size (mm"^2, ")")))) +
scale_fill_manual(name="Lifestage", values=c("#8C510A", "#DFC27D","#80CDC1", "#003C30"))+
scale_color_manual(name="Lifestage", values=c("#8C510A", "#DFC27D","#80CDC1", "#003C30"))+
xlab(expression(bold(paste("Individual Size (mm"^2, ")"))))+
ylab(expression(bold(paste("Symbiont cells individual"^-1))))+
#ylim(0, 9000)+
theme_classic() +
theme(
legend.position="none",
axis.title=element_text(face="bold", size=14),
axis.text=element_text(size=12, color="black"),
legend.title=element_text(face="bold", size=14)
); correlation
Test relationship with a spearman correlation.
cor.test(corr$mean_area, corr$cells.ind, method=c("spearman"))
##
## Spearman's rank correlation rho
##
## data: corr$mean_area and corr$cells.ind
## S = 6658, p-value = 0.01753
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.3754221
Significant correlation between size and cell counts. r=0.37, p=0.017
Plot cells per mm^2 as a boxplot.
#order for creating a legend for all plots
corr$group <- factor(corr$group, levels = c("Egg", "Embryo", "Larvae", "Metamorphosed Recruit", "Attached Recruit"))
cells_size_plot<-corr %>%
ggplot(., aes(x = hpf, y = counts_area)) +
geom_boxplot(aes(color=group), outlier.size = 0, lwd=1) +
geom_point(aes(fill=group), pch = 21, size=4, position = position_jitterdodge(0.4)) +
xlab("Hours Post-Fertilization") +
scale_fill_manual(name="Lifestage", values=c("#8C510A", "#DFC27D","#80CDC1", "#003C30", "#BA55D3"), guide="none")+
scale_color_manual(name="Lifestage", values=c("#8C510A", "#DFC27D","#80CDC1", "#003C30", "#BA55D3"))+
ylab(expression(bold(paste("Symbiont cells mm"^-2))))+
#ylim(2000, 35000)+
theme_classic() +
theme(
legend.position="right",
axis.title=element_text(face="bold", size=14),
axis.text=element_text(size=12, color="black"),
legend.title=element_text(face="bold", size=14)
); cells_size_plot
Plot as linear relationship.
cells_size_plot2<-corr %>%
ggplot(., aes(x = as.numeric(as.character(hpf)), y = counts_area)) +
#geom_boxplot(aes(color=group), outlier.size = 0, lwd=1) +
geom_point(aes(fill=group, group=group), pch = 21, size=4, position = position_jitterdodge(0.4)) +
geom_smooth(method="lm", se=TRUE, fullrange=TRUE, level=0.95, color="black") +
xlab("Hours Post-Fertilization") +
scale_fill_manual(name="Lifestage", values=c("#8C510A", "#DFC27D","#80CDC1", "#003C30", "#BA55D3"), guide="none")+
scale_color_manual(name="Lifestage", values=c( "#8C510A", "#DFC27D","#80CDC1", "#003C30", "#BA55D3"))+
ylab(expression(bold(paste("Symbiont cells mm"^-2))))+
#ylim(2000, 35000)+
theme_classic() +
theme(
legend.position="right",
axis.title=element_text(face="bold", size=14),
axis.text=element_text(size=12, color="black"),
legend.title=element_text(face="bold", size=14)
); cells_size_plot2
Analyze differences in normalized cell counts by timepoint.
model<-corr%>%
#filter(!group=="Attached Recruit")%>%
#droplevels()%>%
aov(counts_area~lifestage, data=.)
qqPlot(residuals(model))
## [1] 41 30
corr%>%
#filter(!group=="Attached Recruit")%>%
#droplevels()%>%
leveneTest(residuals(model)~lifestage, data=.)
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 12 1.2849 0.2704
## 35
summary(model)
## Df Sum Sq Mean Sq F value Pr(>F)
## lifestage 12 3.847e+09 320569728 26.84 4.08e-14 ***
## Residuals 35 4.180e+08 11943131
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
View posthoc differences.
emm = emmeans(model, ~ lifestage)
cld(emm, Letters=c(LETTERS)) #letter display
## lifestage emmean SE df lower.CL upper.CL .group
## Larvae 1 5285 1728 35 1777 8793 A
## Egg Fertilized 7746 1728 35 4238 11254 AB
## Embryo 1 9923 1728 35 6416 13431 ABC
## Larvae 4 14732 1728 35 11224 18240 BCD
## Larvae 3 14756 1728 35 11248 18264 BCD
## Larvae 6 16510 1728 35 13002 20018 CDE
## Larvae 2 16867 1728 35 13359 20374 CDE
## Larvae 5 17732 1728 35 14224 21240 CDE
## Plug 1 21503 1995 35 17452 25554 DEF
## Recruit 2 23301 1728 35 19793 26809 DEF
## Recruit 1 25137 1728 35 21629 28644 EF
## Plug 2 30977 2444 35 26016 35938 FG
## Plug 3 42014 1995 35 37964 46065 G
##
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 13 estimates
## significance level used: alpha = 0.05
## NOTE: Compact letter displays can be misleading
## because they show NON-findings rather than findings.
## Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
Generate summary table of descriptive statistics.
#need to output to csv
corr%>%
group_by(group, hpf, lifestage)%>%
summarise(n=length(counts_area),
Mean_sym_mm2=format(round(mean(counts_area), 0), 0),
SE=format(round(sd(counts_area)/sqrt(length(counts_area)),0),0))%>%
rename(Lifestage=group, HPF=hpf)%>%
write_csv(., "Mcap2020/Output/Physiology/normalized_size_cells_summary.csv")
Generate physiology panel with all variables of interest.
# extract the legend from one of the plots
legend <- get_legend(
# create some space to the left of the legend
cells_size_plot + theme(legend.box.margin = margin(1,1,1,1))
)
#remove legends from plots
size_plot2<-size_plot2+theme(legend.position="none")
symb_plot2<-symb_plot2+theme(legend.position="none")
r_corr_plot<-r_corr_plot+theme(legend.position="none")
cells_size_plot_l<-cells_size_plot+theme(legend.position="none")
#assemble plots
all_plots<-plot_grid(size_plot2, symb_plot2, cells_size_plot_l, r_corr_plot, labels = c("A", "B", "C", "D"), label_size=18, ncol=4, nrow=1, rel_heights= c(1,1,1,1), rel_widths = c(1,1,1,0.8), align="h")
all_plots_legend<-plot_grid(all_plots, legend, rel_widths = c(4, 0.5), ncol=2, nrow=1)
ggsave(file="Mcap2020/Figures/Physiology/Physiology_figure.pdf", all_plots_legend, dpi=300, width=24, height=6, units="in")
ggsave(file="Mcap2020/Figures/Physiology/Physiology_figure.png", all_plots_legend, dpi=300, width=24, height=6, units="in")
Early life history physiology